The aim of single image SR (SISR) is to obtain a high resolution (HR) image from its low resolution (LR) version. In order to achieve this goal, many deep learning-based algorithms have been proposed. Among them, Dong et al. [1]}, [2]} first used a convolutional neural network (CNN) to enhance the resolution of an image. They proposed a three-layer convolutional network named SRCNN to predict HR image and achieved superior performance over traditional methods. Afterwards, many deeper and wider network structures [3]}, [4]}, [5]} were designed to improve the performance of SISR. Lim et al. [6]} stacked modified residual blocks to construct a deep SR network named EDSR which won the champion of the NTIRE 2017 SISR challenge [7]}. Ma et al. [8]} aggregated dense hierarchical features progressively in a tree structure to improve SISR performance. Liu et al. [9]} aggregated the local residual features to extract more powerful features for SISR. Zhang et al. [10]} proposed a residual dense network (RDN) with skip connections to fully use hierarchical feature representations. Lai et al. [11]} developed a pyramidal framework to construct a deep laplacian pyramid super-resolution network with three sub-networks. The input LR image was added to the output of each sub-network to obtain SR results. Recently, several comprehensive reviews on SISR [12]}, [13]}, [14]} have been published.
